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<meta name="description" content="Metrics to assess performance on sequence labeling task given prediction
Functions named as ``*_score`` return a scalar value to maximize: the higher
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<header>
<h1 class="title">Module <code>ktrain.text.ner.metrics</code></h1>
</header>
<section id="section-intro">
<p>Metrics to assess performance on sequence labeling task given prediction
Functions named as <code>*_score</code> return a scalar value to maximize: the higher
the better
Reference: seqeval==0.0.19</p>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">"""Metrics to assess performance on sequence labeling task given prediction
Functions named as ``*_score`` return a scalar value to maximize: the higher
the better
Reference: seqeval==0.0.19
"""
from __future__ import absolute_import, division, print_function
import warnings
from collections import defaultdict
import numpy as np
def get_entities(seq, suffix=False):
"""Gets entities from sequence.
Args:
seq (list): sequence of labels.
Returns:
list: list of (chunk_type, chunk_start, chunk_end).
Example:
>>> from seqeval.metrics.sequence_labeling import get_entities
>>> seq = ['B-PER', 'I-PER', 'O', 'B-LOC']
>>> get_entities(seq)
[('PER', 0, 1), ('LOC', 3, 3)]
"""
def _validate_chunk(chunk, suffix):
if chunk in ["O", "B", "I", "E", "S"]:
return
if suffix:
if not (
chunk.endswith("-B")
or chunk.endswith("-I")
or chunk.endswith("-E")
or chunk.endswith("-S")
):
warnings.warn("{} seems not to be NE tag.".format(chunk))
else:
if not (
chunk.startswith("B-")
or chunk.startswith("I-")
or chunk.startswith("E-")
or chunk.startswith("S-")
):
warnings.warn("{} seems not to be NE tag.".format(chunk))
# for nested list
if any(isinstance(s, list) for s in seq):
seq = [item for sublist in seq for item in sublist + ["O"]]
prev_tag = "O"
prev_type = ""
begin_offset = 0
chunks = []
for i, chunk in enumerate(seq + ["O"]):
_validate_chunk(chunk, suffix)
if suffix:
tag = chunk[-1]
type_ = chunk[:-1].rsplit("-", maxsplit=1)[0] or "_"
else:
tag = chunk[0]
type_ = chunk[1:].split("-", maxsplit=1)[-1] or "_"
if end_of_chunk(prev_tag, tag, prev_type, type_):
chunks.append((prev_type, begin_offset, i - 1))
if start_of_chunk(prev_tag, tag, prev_type, type_):
begin_offset = i
prev_tag = tag
prev_type = type_
return chunks
def end_of_chunk(prev_tag, tag, prev_type, type_):
"""Checks if a chunk ended between the previous and current word.
Args:
prev_tag: previous chunk tag.
tag: current chunk tag.
prev_type: previous type.
type_: current type.
Returns:
chunk_end: boolean.
"""
chunk_end = False
if prev_tag == "E":
chunk_end = True
if prev_tag == "S":
chunk_end = True
if prev_tag == "B" and tag == "B":
chunk_end = True
if prev_tag == "B" and tag == "S":
chunk_end = True
if prev_tag == "B" and tag == "O":
chunk_end = True
if prev_tag == "I" and tag == "B":
chunk_end = True
if prev_tag == "I" and tag == "S":
chunk_end = True
if prev_tag == "I" and tag == "O":
chunk_end = True
if prev_tag != "O" and prev_tag != "." and prev_type != type_:
chunk_end = True
return chunk_end
def start_of_chunk(prev_tag, tag, prev_type, type_):
"""Checks if a chunk started between the previous and current word.
Args:
prev_tag: previous chunk tag.
tag: current chunk tag.
prev_type: previous type.
type_: current type.
Returns:
chunk_start: boolean.
"""
chunk_start = False
if tag == "B":
chunk_start = True
if tag == "S":
chunk_start = True
if prev_tag == "E" and tag == "E":
chunk_start = True
if prev_tag == "E" and tag == "I":
chunk_start = True
if prev_tag == "S" and tag == "E":
chunk_start = True
if prev_tag == "S" and tag == "I":
chunk_start = True
if prev_tag == "O" and tag == "E":
chunk_start = True
if prev_tag == "O" and tag == "I":
chunk_start = True
if tag != "O" and tag != "." and prev_type != type_:
chunk_start = True
return chunk_start
def f1_score(y_true, y_pred, average="micro", suffix=False):
"""Compute the F1 score.
The F1 score can be interpreted as a weighted average of the precision and
recall, where an F1 score reaches its best value at 1 and worst score at 0.
The relative contribution of precision and recall to the F1 score are
equal. The formula for the F1 score is::
F1 = 2 * (precision * recall) / (precision + recall)
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.
Returns:
score : float.
Example:
>>> from seqeval.metrics import f1_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> f1_score(y_true, y_pred)
0.50
"""
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
nb_correct = len(true_entities & pred_entities)
nb_pred = len(pred_entities)
nb_true = len(true_entities)
p = nb_correct / nb_pred if nb_pred > 0 else 0
r = nb_correct / nb_true if nb_true > 0 else 0
score = 2 * p * r / (p + r) if p + r > 0 else 0
return score
def accuracy_score(y_true, y_pred):
"""Accuracy classification score.
In multilabel classification, this function computes subset accuracy:
the set of labels predicted for a sample must *exactly* match the
corresponding set of labels in y_true.
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.
Returns:
score : float.
Example:
>>> from seqeval.metrics import accuracy_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> accuracy_score(y_true, y_pred)
0.80
"""
if any(isinstance(s, list) for s in y_true):
y_true = [item for sublist in y_true for item in sublist]
y_pred = [item for sublist in y_pred for item in sublist]
nb_correct = sum(y_t == y_p for y_t, y_p in zip(y_true, y_pred))
nb_true = len(y_true)
score = nb_correct / nb_true
return score
def precision_score(y_true, y_pred, average="micro", suffix=False):
"""Compute the precision.
The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
true positives and ``fp`` the number of false positives. The precision is
intuitively the ability of the classifier not to label as positive a sample.
The best value is 1 and the worst value is 0.
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.
Returns:
score : float.
Example:
>>> from seqeval.metrics import precision_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> precision_score(y_true, y_pred)
0.50
"""
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
nb_correct = len(true_entities & pred_entities)
nb_pred = len(pred_entities)
score = nb_correct / nb_pred if nb_pred > 0 else 0
return score
def recall_score(y_true, y_pred, average="micro", suffix=False):
"""Compute the recall.
The recall is the ratio ``tp / (tp + fn)`` where ``tp`` is the number of
true positives and ``fn`` the number of false negatives. The recall is
intuitively the ability of the classifier to find all the positive samples.
The best value is 1 and the worst value is 0.
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.
Returns:
score : float.
Example:
>>> from seqeval.metrics import recall_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> recall_score(y_true, y_pred)
0.50
"""
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
nb_correct = len(true_entities & pred_entities)
nb_true = len(true_entities)
score = nb_correct / nb_true if nb_true > 0 else 0
return score
def performance_measure(y_true, y_pred):
"""
Compute the performance metrics: TP, FP, FN, TN
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.
Returns:
performance_dict : dict
Example:
>>> from seqeval.metrics import performance_measure
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'O', 'B-ORG'], ['B-PER', 'I-PER', 'O', 'B-PER']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O', 'O'], ['B-PER', 'I-PER', 'O', 'B-MISC']]
>>> performance_measure(y_true, y_pred)
{'TP': 3, 'FP': 3, 'FN': 1, 'TN': 4}
"""
performance_dict = dict()
if any(isinstance(s, list) for s in y_true):
y_true = [item for sublist in y_true for item in sublist]
y_pred = [item for sublist in y_pred for item in sublist]
performance_dict["TP"] = sum(
y_t == y_p for y_t, y_p in zip(y_true, y_pred) if ((y_t != "O") or (y_p != "O"))
)
performance_dict["FP"] = sum(
((y_t != y_p) and (y_p != "O")) for y_t, y_p in zip(y_true, y_pred)
)
performance_dict["FN"] = sum(
((y_t != "O") and (y_p == "O")) for y_t, y_p in zip(y_true, y_pred)
)
performance_dict["TN"] = sum(
(y_t == y_p == "O") for y_t, y_p in zip(y_true, y_pred)
)
return performance_dict
def classification_report(y_true, y_pred, digits=2, suffix=False, output_dict=False):
"""Build a text report showing the main classification metrics.
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a classifier.
digits : int. Number of digits for formatting output floating point values.
output_dict : bool(default=False). If True, return output as dict else str.
Returns:
report : string/dict. Summary of the precision, recall, F1 score for each class.
Examples:
>>> from seqeval.metrics import classification_report
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> print(classification_report(y_true, y_pred))
precision recall f1-score support
<BLANKLINE>
MISC 0.00 0.00 0.00 1
PER 1.00 1.00 1.00 1
<BLANKLINE>
micro avg 0.50 0.50 0.50 2
macro avg 0.50 0.50 0.50 2
weighted avg 0.50 0.50 0.50 2
<BLANKLINE>
"""
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
name_width = 0
d1 = defaultdict(set)
d2 = defaultdict(set)
for e in true_entities:
d1[e[0]].add((e[1], e[2]))
name_width = max(name_width, len(e[0]))
for e in pred_entities:
d2[e[0]].add((e[1], e[2]))
avg_types = ["micro avg", "macro avg", "weighted avg"]
if output_dict:
report_dict = dict()
else:
avg_width = max([len(x) for x in avg_types])
width = max(name_width, avg_width, digits)
headers = ["precision", "recall", "f1-score", "support"]
head_fmt = "{:>{width}s} " + " {:>9}" * len(headers)
report = head_fmt.format("", *headers, width=width)
report += "\n\n"
row_fmt = "{:>{width}s} " + " {:>9.{digits}f}" * 3 + " {:>9}\n"
ps, rs, f1s, s = [], [], [], []
for type_name in sorted(d1.keys()):
true_entities = d1[type_name]
pred_entities = d2[type_name]
nb_correct = len(true_entities & pred_entities)
nb_pred = len(pred_entities)
nb_true = len(true_entities)
p = nb_correct / nb_pred if nb_pred > 0 else 0
r = nb_correct / nb_true if nb_true > 0 else 0
f1 = 2 * p * r / (p + r) if p + r > 0 else 0
if output_dict:
report_dict[type_name] = {
"precision": p,
"recall": r,
"f1-score": f1,
"support": nb_true,
}
else:
report += row_fmt.format(
*[type_name, p, r, f1, nb_true], width=width, digits=digits
)
ps.append(p)
rs.append(r)
f1s.append(f1)
s.append(nb_true)
if not output_dict:
report += "\n"
# compute averages
nb_true = np.sum(s)
for avg_type in avg_types:
if avg_type == "micro avg":
# micro average
p = precision_score(y_true, y_pred, suffix=suffix)
r = recall_score(y_true, y_pred, suffix=suffix)
f1 = f1_score(y_true, y_pred, suffix=suffix)
elif avg_type == "macro avg":
# macro average
p = np.average(ps)
r = np.average(rs)
f1 = np.average(f1s)
elif avg_type == "weighted avg":
# weighted average
p = np.average(ps, weights=s)
r = np.average(rs, weights=s)
f1 = np.average(f1s, weights=s)
else:
assert False, "unexpected average: {}".format(avg_type)
if output_dict:
report_dict[avg_type] = {
"precision": p,
"recall": r,
"f1-score": f1,
"support": nb_true,
}
else:
report += row_fmt.format(
*[avg_type, p, r, f1, nb_true], width=width, digits=digits
)
if output_dict:
return report_dict
else:
return report</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.text.ner.metrics.accuracy_score"><code class="name flex">
<span>def <span class="ident">accuracy_score</span></span>(<span>y_true, y_pred)</span>
</code></dt>
<dd>
<div class="desc"><p>Accuracy classification score.</p>
<p>In multilabel classification, this function computes subset accuracy:
the set of labels predicted for a sample must <em>exactly</em> match the
corresponding set of labels in y_true.</p>
<h2 id="args">Args</h2>
<p>y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.</p>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>score </code></dt>
<dd>float.</dd>
</dl>
<h2 id="example">Example</h2>
<pre><code class="language-python-repl">>>> from seqeval.metrics import accuracy_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> accuracy_score(y_true, y_pred)
0.80
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def accuracy_score(y_true, y_pred):
"""Accuracy classification score.
In multilabel classification, this function computes subset accuracy:
the set of labels predicted for a sample must *exactly* match the
corresponding set of labels in y_true.
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.
Returns:
score : float.
Example:
>>> from seqeval.metrics import accuracy_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> accuracy_score(y_true, y_pred)
0.80
"""
if any(isinstance(s, list) for s in y_true):
y_true = [item for sublist in y_true for item in sublist]
y_pred = [item for sublist in y_pred for item in sublist]
nb_correct = sum(y_t == y_p for y_t, y_p in zip(y_true, y_pred))
nb_true = len(y_true)
score = nb_correct / nb_true
return score</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.metrics.classification_report"><code class="name flex">
<span>def <span class="ident">classification_report</span></span>(<span>y_true, y_pred, digits=2, suffix=False, output_dict=False)</span>
</code></dt>
<dd>
<div class="desc"><p>Build a text report showing the main classification metrics.</p>
<h2 id="args">Args</h2>
<p>y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a classifier.
digits : int. Number of digits for formatting output floating point values.
output_dict : bool(default=False). If True, return output as dict else str.</p>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>report </code></dt>
<dd>string/dict. Summary of the precision, recall, F1 score for each class.</dd>
</dl>
<h2 id="examples">Examples</h2>
<blockquote>
<blockquote>
<blockquote>
<p>from seqeval.metrics import classification_report
y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
print(classification_report(y_true, y_pred))
precision
recall
f1-score
support
<BLANKLINE>
MISC
0.00
0.00
0.00
1
PER
1.00
1.00
1.00
1
<BLANKLINE>
micro avg
0.50
0.50
0.50
2
macro avg
0.50
0.50
0.50
2
weighted avg
0.50
0.50
0.50
2
<BLANKLINE></p>
</blockquote>
</blockquote>
</blockquote></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def classification_report(y_true, y_pred, digits=2, suffix=False, output_dict=False):
"""Build a text report showing the main classification metrics.
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a classifier.
digits : int. Number of digits for formatting output floating point values.
output_dict : bool(default=False). If True, return output as dict else str.
Returns:
report : string/dict. Summary of the precision, recall, F1 score for each class.
Examples:
>>> from seqeval.metrics import classification_report
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> print(classification_report(y_true, y_pred))
precision recall f1-score support
<BLANKLINE>
MISC 0.00 0.00 0.00 1
PER 1.00 1.00 1.00 1
<BLANKLINE>
micro avg 0.50 0.50 0.50 2
macro avg 0.50 0.50 0.50 2
weighted avg 0.50 0.50 0.50 2
<BLANKLINE>
"""
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
name_width = 0
d1 = defaultdict(set)
d2 = defaultdict(set)
for e in true_entities:
d1[e[0]].add((e[1], e[2]))
name_width = max(name_width, len(e[0]))
for e in pred_entities:
d2[e[0]].add((e[1], e[2]))
avg_types = ["micro avg", "macro avg", "weighted avg"]
if output_dict:
report_dict = dict()
else:
avg_width = max([len(x) for x in avg_types])
width = max(name_width, avg_width, digits)
headers = ["precision", "recall", "f1-score", "support"]
head_fmt = "{:>{width}s} " + " {:>9}" * len(headers)
report = head_fmt.format("", *headers, width=width)
report += "\n\n"
row_fmt = "{:>{width}s} " + " {:>9.{digits}f}" * 3 + " {:>9}\n"
ps, rs, f1s, s = [], [], [], []
for type_name in sorted(d1.keys()):
true_entities = d1[type_name]
pred_entities = d2[type_name]
nb_correct = len(true_entities & pred_entities)
nb_pred = len(pred_entities)
nb_true = len(true_entities)
p = nb_correct / nb_pred if nb_pred > 0 else 0
r = nb_correct / nb_true if nb_true > 0 else 0
f1 = 2 * p * r / (p + r) if p + r > 0 else 0
if output_dict:
report_dict[type_name] = {
"precision": p,
"recall": r,
"f1-score": f1,
"support": nb_true,
}
else:
report += row_fmt.format(
*[type_name, p, r, f1, nb_true], width=width, digits=digits
)
ps.append(p)
rs.append(r)
f1s.append(f1)
s.append(nb_true)
if not output_dict:
report += "\n"
# compute averages
nb_true = np.sum(s)
for avg_type in avg_types:
if avg_type == "micro avg":
# micro average
p = precision_score(y_true, y_pred, suffix=suffix)
r = recall_score(y_true, y_pred, suffix=suffix)
f1 = f1_score(y_true, y_pred, suffix=suffix)
elif avg_type == "macro avg":
# macro average
p = np.average(ps)
r = np.average(rs)
f1 = np.average(f1s)
elif avg_type == "weighted avg":
# weighted average
p = np.average(ps, weights=s)
r = np.average(rs, weights=s)
f1 = np.average(f1s, weights=s)
else:
assert False, "unexpected average: {}".format(avg_type)
if output_dict:
report_dict[avg_type] = {
"precision": p,
"recall": r,
"f1-score": f1,
"support": nb_true,
}
else:
report += row_fmt.format(
*[avg_type, p, r, f1, nb_true], width=width, digits=digits
)
if output_dict:
return report_dict
else:
return report</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.metrics.end_of_chunk"><code class="name flex">
<span>def <span class="ident">end_of_chunk</span></span>(<span>prev_tag, tag, prev_type, type_)</span>
</code></dt>
<dd>
<div class="desc"><p>Checks if a chunk ended between the previous and current word.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>prev_tag</code></strong></dt>
<dd>previous chunk tag.</dd>
<dt><strong><code>tag</code></strong></dt>
<dd>current chunk tag.</dd>
<dt><strong><code>prev_type</code></strong></dt>
<dd>previous type.</dd>
<dt><strong><code>type_</code></strong></dt>
<dd>current type.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>chunk_end</code></dt>
<dd>boolean.</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def end_of_chunk(prev_tag, tag, prev_type, type_):
"""Checks if a chunk ended between the previous and current word.
Args:
prev_tag: previous chunk tag.
tag: current chunk tag.
prev_type: previous type.
type_: current type.
Returns:
chunk_end: boolean.
"""
chunk_end = False
if prev_tag == "E":
chunk_end = True
if prev_tag == "S":
chunk_end = True
if prev_tag == "B" and tag == "B":
chunk_end = True
if prev_tag == "B" and tag == "S":
chunk_end = True
if prev_tag == "B" and tag == "O":
chunk_end = True
if prev_tag == "I" and tag == "B":
chunk_end = True
if prev_tag == "I" and tag == "S":
chunk_end = True
if prev_tag == "I" and tag == "O":
chunk_end = True
if prev_tag != "O" and prev_tag != "." and prev_type != type_:
chunk_end = True
return chunk_end</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.metrics.f1_score"><code class="name flex">
<span>def <span class="ident">f1_score</span></span>(<span>y_true, y_pred, average='micro', suffix=False)</span>
</code></dt>
<dd>
<div class="desc"><p>Compute the F1 score.</p>
<p>The F1 score can be interpreted as a weighted average of the precision and
recall, where an F1 score reaches its best value at 1 and worst score at 0.
The relative contribution of precision and recall to the F1 score are
equal. The formula for the F1 score is::</p>
<pre><code>F1 = 2 * (precision * recall) / (precision + recall)
</code></pre>
<h2 id="args">Args</h2>
<p>y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.</p>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>score </code></dt>
<dd>float.</dd>
</dl>
<h2 id="example">Example</h2>
<pre><code class="language-python-repl">>>> from seqeval.metrics import f1_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> f1_score(y_true, y_pred)
0.50
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def f1_score(y_true, y_pred, average="micro", suffix=False):
"""Compute the F1 score.
The F1 score can be interpreted as a weighted average of the precision and
recall, where an F1 score reaches its best value at 1 and worst score at 0.
The relative contribution of precision and recall to the F1 score are
equal. The formula for the F1 score is::
F1 = 2 * (precision * recall) / (precision + recall)
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.
Returns:
score : float.
Example:
>>> from seqeval.metrics import f1_score
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'I-MISC', 'O'], ['B-PER', 'I-PER', 'O']]
>>> f1_score(y_true, y_pred)
0.50
"""
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
nb_correct = len(true_entities & pred_entities)
nb_pred = len(pred_entities)
nb_true = len(true_entities)
p = nb_correct / nb_pred if nb_pred > 0 else 0
r = nb_correct / nb_true if nb_true > 0 else 0
score = 2 * p * r / (p + r) if p + r > 0 else 0
return score</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.metrics.get_entities"><code class="name flex">
<span>def <span class="ident">get_entities</span></span>(<span>seq, suffix=False)</span>
</code></dt>
<dd>
<div class="desc"><p>Gets entities from sequence.</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>seq</code></strong> : <code>list</code></dt>
<dd>sequence of labels.</dd>
</dl>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>list</code></dt>
<dd>list of (chunk_type, chunk_start, chunk_end).</dd>
</dl>
<h2 id="example">Example</h2>
<pre><code class="language-python-repl">>>> from seqeval.metrics.sequence_labeling import get_entities
>>> seq = ['B-PER', 'I-PER', 'O', 'B-LOC']
>>> get_entities(seq)
[('PER', 0, 1), ('LOC', 3, 3)]
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def get_entities(seq, suffix=False):
"""Gets entities from sequence.
Args:
seq (list): sequence of labels.
Returns:
list: list of (chunk_type, chunk_start, chunk_end).
Example:
>>> from seqeval.metrics.sequence_labeling import get_entities
>>> seq = ['B-PER', 'I-PER', 'O', 'B-LOC']
>>> get_entities(seq)
[('PER', 0, 1), ('LOC', 3, 3)]
"""
def _validate_chunk(chunk, suffix):
if chunk in ["O", "B", "I", "E", "S"]:
return
if suffix:
if not (
chunk.endswith("-B")
or chunk.endswith("-I")
or chunk.endswith("-E")
or chunk.endswith("-S")
):
warnings.warn("{} seems not to be NE tag.".format(chunk))
else:
if not (
chunk.startswith("B-")
or chunk.startswith("I-")
or chunk.startswith("E-")
or chunk.startswith("S-")
):
warnings.warn("{} seems not to be NE tag.".format(chunk))
# for nested list
if any(isinstance(s, list) for s in seq):
seq = [item for sublist in seq for item in sublist + ["O"]]
prev_tag = "O"
prev_type = ""
begin_offset = 0
chunks = []
for i, chunk in enumerate(seq + ["O"]):
_validate_chunk(chunk, suffix)
if suffix:
tag = chunk[-1]
type_ = chunk[:-1].rsplit("-", maxsplit=1)[0] or "_"
else:
tag = chunk[0]
type_ = chunk[1:].split("-", maxsplit=1)[-1] or "_"
if end_of_chunk(prev_tag, tag, prev_type, type_):
chunks.append((prev_type, begin_offset, i - 1))
if start_of_chunk(prev_tag, tag, prev_type, type_):
begin_offset = i
prev_tag = tag
prev_type = type_
return chunks</code></pre>
</details>
</dd>
<dt id="ktrain.text.ner.metrics.performance_measure"><code class="name flex">
<span>def <span class="ident">performance_measure</span></span>(<span>y_true, y_pred)</span>
</code></dt>
<dd>
<div class="desc"><p>Compute the performance metrics: TP, FP, FN, TN</p>
<h2 id="args">Args</h2>
<p>y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.</p>
<h2 id="returns">Returns</h2>
<dl>
<dt><code>performance_dict </code></dt>
<dd>dict</dd>
</dl>
<h2 id="example">Example</h2>
<pre><code class="language-python-repl">>>> from seqeval.metrics import performance_measure
>>> y_true = [['O', 'O', 'O', 'B-MISC', 'I-MISC', 'O', 'B-ORG'], ['B-PER', 'I-PER', 'O', 'B-PER']]
>>> y_pred = [['O', 'O', 'B-MISC', 'I-MISC', 'I-MISC', 'O', 'O'], ['B-PER', 'I-PER', 'O', 'B-MISC']]
>>> performance_measure(y_true, y_pred)
{'TP': 3, 'FP': 3, 'FN': 1, 'TN': 4}
</code></pre></div>
<details class="source">
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<span>Expand source code</span>
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<pre><code class="python">def performance_measure(y_true, y_pred):
"""
Compute the performance metrics: TP, FP, FN, TN
Args:
y_true : 2d array. Ground truth (correct) target values.
y_pred : 2d array. Estimated targets as returned by a tagger.